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ANALYTICS WILL REJUVENATE FINANCIAL SERVICES AFTER THE DEATH OF THIRD-PARTY COOKIES

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Dave Hendry, Regional Sales Director, Fanplayr

 

The decision by Google and the other major browser companies to axe third-party cookies deserves close attention from financial services companies.

Coming fully into force next year, the move will effectively end the traditional supply of data that has enabled personalisation, optimised website interactions and driven internet advertising. A company will no longer be able to build a picture of individuals’ habits and preferences by using a cookie to track where its web visitors go once they have left its site.

The reason the big browser companies have called a halt to third-party cookies is because of their fears about infringement of legislation such as the EU’s GDPR (General Data Protection Regulation) and the CCPA (California Consumer Privacy Act) in California.

In theory, the end of third-party cookies has come just at the wrong time, as millions of people shift to online or mobile banking in large numbers. In the UK, for example, more than three-quarters of Britons now use online banking and 14 million use digital-only banks.

 

Dave Hendry

A major opportunity to improve web interactions with newer technology

It seems drastic, but it is actually an opportunity for financial organisations to improve how they interact with expanding numbers of web visitors and customers using newer technologies. Behavioural analytics driven by AI, for example, is a technology that offers far superior, real-time capabilities when compared with the conventional use of third-party cookie data. These analytics solutions use customer behaviour data generated by financial organisations’ own website domains and where available, correlate it with data from customers’ transaction histories.

The result is a solution that is faster, more accurate and responsive than conventional technology relying on cookie data owned and stored by third-party organisations. Instead of rigid profiling and personalisation, behavioural analytics enables real-time interactions based on a more dynamic picture of how an individual’s requirements are changing.

Using a first-party cookie of the type employed by Facebook, behavioural analytics solutions examine a customer’s browsing characteristics including time on site, speed of movement and page views, as well as more obvious features such as interest in specific products. Historical data added to the analysis includes what customers did on previous visits and the interval between those visits, establishing patterns where possible.

 

Segmentation for better targeting

Segmentation allows a bank to identify customers as soon as they arrive on its site, according to whether they are a new or existing customer. Their behaviour then indicates what they want.

Knowing what customers are interested in is important. Customers visit financial services websites for a host of reasons – from seeking information, to opening accounts or exploring loans and mortgage offers. They may also want advice about investments and savings, pensions or small business finance. Almost all of these requirements involve quite complex mental processes which financial organisations can influence while consumers are on their sites.

 

Hubs that make insights actionable

Collecting the data is not difficult – the skill is in making it actionable in an effective way, replicating the ability of a perceptive employee to read a customer’s state of mind. Banks can do this by setting up a behavioural analytics hub to understand what a customer’s behaviour means and how it can be optimised.

Using customised parameters, the hub will, for instance, trigger a screen notification that prompts the web visitor to fill in a form requesting an appointment. In the case of existing customers, the technology can correlate health insurance offers with spending on fitness, and, in general, savings and investment recommendations can be tailored to the client’s concerns or goals as revealed by their navigation of the website or mobile app.

Banks can set up analytics to see when consumers are behaving in a way that indicates they about to leave the website, allowing them to intervene with a notification that could include an offer. This provides a positive outcome and avoids the blanket use of offers that undermines profitability.

It is a more sophisticated and personalised approach that avoids annoying pop-ups or recommendations that fail to match individual preferences. As part of a single AI-powered segmentation platform, the technology enables banks to personalise marketing content in SMS messages and emails sent to consumers (who consent), which deliver far better results through precise targeting.

 

Solutions for last-mile interaction in the open banking era

The single platform approach also has another major advantage. It is much easier to implement and far more efficient and streamlined compared with separate solutions for different parts of the customer journey.

The benefits of using AI-powered segmentation solutions should be part of the financial sector’s broader strategy to transform its systems for the open banking era as we approach the end of third-party cookies. It is almost a commonplace to say that banks continue to struggle with the complexity of their systems, undermining their ability to deliver a high-quality last mile for consumers. This is one headache they can now resolve without huge disruption or investment.

The alternative is to risk losing track of customers. Behavioural analytics, by contrast, will deliver new insights into customers that are better than third-party cookie data, being more accurate and actionable in real time. Financial services organisations need to employ the latest advances in AI-powered behavioural analytics if they are serious about providing a slick and personalised service to customers that doesn’t break the bank.

 

Business

Mitigating the insurance risks of climate change through geospatial data visualisation

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By

Richard Toomey, Senior Manager, Commercial Insurance at LexisNexis Risk Solutions UK and Ireland

 

In the lead up to the 26th United Nations Climate Change Conference of the Parties (COP26)[i] November 2021, A United in Science report[ii]  provided a stark warning of the impact and acceleration of climate change. The UK Environment Agency also warned of more extreme weather leading to increased flooding and drought[iii]. While some progress was made at the conference, understanding the changing risks created by extreme weather to price property insurance more effectively, and more importantly, to help mitigate the physical risks posed by climate change, has become imperative.

Mapped geospatial data intelligence including live data on flood warnings and river flows, viewed alongside data held by insurance providers on the properties in their portfolio, can be a key ally in helping to protect customers and reduce claims losses created by extreme weather events.

With the air temperature rising and heavy rain becoming more and more frequent due to climate change insurance providers are looking to identify properties that are more at risk than others. For example, properties with basements carry more of a substantial risk of surface water claims than others and especially in London where space is tight and water runoff is low. In the autumn of 2021, the industry saw a number of high value claims due to basement flooding. There are some really large high net worth (HNW) households with big basements which carry a significant insurance risk.  The problem is that in many cases insurance providers don’t know if they have a property ‘on cover’ that actually has a basement.

The huge and growing volume of data now available to the insurance market to assess property risk to the level the industry needs, could easily overwhelm and prove a barrier to the swift decisions needed in weather-related surge events. However, the evolution of desktop based geospatial data visualisation tools such as LexisNexis® Map View means insurance providers can make quick, informed decisions based on a picture or map of risk, looking at a specific geographical region, a postcode, an address or a single property outline.

They can look at environmental risks including flood, fire and subsidence and live flood data updated every 15 minutes direct from the Environment Agency, as well as highly predictive flood risk data from respected flood modelling organisations. Insurance providers can also bring in data on the characteristics of a property to understand more about its construction, including the type of roof it has, how many floors there are, the square footage, as well as further data on the location and the individuals behind a business to gain a more holistic understanding of risk for pricing.

Mapping of historical flood data brings a further dimension to the understanding of risk, revealing the maximum extent of all individually recorded flood outlines from rivers, the sea and groundwater springs in England and Wales. This takes into account the presence of defences, structures, and other infrastructure where they existed at the time of flooding and includes floods where overtopping, such as at seawalls, river breaches or blockages may have occurred.

But the real step-change for the market has been recent ability to view live flood and other environmental data in tandem with customer and policy data held within an insurance providers’ own databases.

Crucially, this means insurance providers can pinpoint down to individual properties, the policyholders most at risk as weather events unfold, should a river burst its banks, or a flood barrier fail and those properties that may actually be vacant at the time of the event.

Through data visualisation tools, insurance providers can gauge where flood water may go so that policyholders can be warned to take measures to protect themselves, their possessions and to move any vehicles to higher ground. They can even see where roads may have been closed due to fallen trees. All this intelligence helps with planning on the ground resources, working with local authorities and claims adjusters. Then, in the immediate aftermath, rather than wait for a deluge of claims, insurance providers are in a position to reach out to customers known to be in areas affected to support them through the claims process.

The inherent flexibility of today’s geospatial data visualisation tools for the insurance market means risk can be assessed as needed or as constant monitor for a whole commercial property portfolio. Fundamentally these tools are designed to streamline the assessment of property risk.

In the future, commercial and residential property claims data gathered from the whole of the market may allow insurance providers to look at a whole portfolio alongside past claims, but for now they can bring in their own claims data to build a more granular picture of risk, to price more accurately and understand how they could help mitigate future claims and potential losses caused by weather events.

A picture can say a thousand words and data visualisation tools can certainly make highly complex risk data easy to understand and act upon. Being able to instantly visualise an environmental risk to policyholders – day or night – using highly granular data on past and present flood events puts insurance providers in a more powerful position to reduce the misery and costs caused by extreme weather.

[i] https://ukcop26. org/wp-content/uploads/2021/07/COP26-Explained. pdf

[ii] https://public. wmo. int/en/media/press-release/climate-change-and-impacts-accelerate

[iii] https://www. gov. uk/government/news/adapt-or-die-says-environment-agency – The Environment Agency’s third adaptation report October 2021

 

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What should you be know about PAN data in PCI DSS?

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Narendra Sahoo (PCI QSA, PCI QPA, CISSP, CISA, CRISC) is the Founder and Director of VISTA InfoSec

 

Introduction

PAN Number or Primary Account Number as we call it is a very sensitive data often used when making online payments or transactions. Customers often share this data with merchants from whom they purchase products or services online. However, customers do expect the merchants and financial institutes to protect the data and prevent incidents of threat. Storing the PAN data for most merchants is a necessity as they may have a legitimate business reason to store cardholder data. But storing PAN data has its share of risk on a business’s network security. Over the years businesses have been storing this data on their server for easy and quick access without realizing the risk it holds and the impact it may have on business.

In fact, most of the data breach incidents that have occurred over the years are due to the storage of unencrypted PAN data on the merchant’s/Service Provider’s servers. While the PCI Council clearly states not to store PAN data yet most merchants for increased consumer convenience store PAN data on their network. Storing customer’s PAN data increases the security risk and, also increases the scope of PCI compliance. So, unless businesses have a legit commercial reason to store PAN data, should not store it. Covering more on this in detail we have today shared details about PAN data and PCI DSS that businesses must know to ensure compliance. So, before getting straight to it let us understand the term PAN Data.

 

What is PAN Data?

PAN Data is basically the 15 or 16 digit numbers on the front of your debit/credit card which is also known as the Primary Account Number. They are also called payment card numbers and are often found on payment cards like credit and debit cards. The PAN account number is printed or embossed on the front of this payment card. The PAN number is issued by customers to merchants at the Point of Sale (POS) that identifies the issuer and the cardholder account while making payments. Customers when making an online purchase share the PAN number to make payments online. These PAN details are used by the merchants to process the payments online.

 

How does PAN Impact PCI DSS Compliance?

Payment Card Industry Data Security Standard clearly states that merchants dealing with online payments or accepting credit/debit card payments must avoid storing sensitive PAN numbers. The PCI DSS Requirement 3 addresses the protection of stored cardholder data. So, considering the storage of PAN data will automatically increase the scope of PCI DSS Compliance for the merchants. This way merchants will have to take additional measures for securing the stored PAN data in the network.

Storing unencrypted PAN data on the network will increase the potential risk of breach and end up having a significant impact on business. It is therefore necessary to secure PAN Data in form of encryption or other techniques as suggested in PCI DSS requirements. Explaining the requirement we have shared the PCI DSS data storage requirements in detail.

 

PAN Data storage in PCI DSS

Merchants may at times for commercial purposes may have to store PAN Data in their server. For these reasons, they will have to take extra precautions and implement additional measures to ensure the security of data and compliance with PCI DSS. The PCI Council outlines the requirement of encryption of cardholder data stored with the merchant. However, it is important to note that not all elements of cardholder need to be encrypted when stored on the server. It is only the PAN data that needs to be encrypted, the rest of the Sensitive Authentication Data (SAD) such as Stripe Data, are not allowed to be even stored by merchants.

What is more important to know and understand about PAN Data storage is that the only times that PAN is not considered to be cardholder data would be when details such as the the cardholder’s name and/or expiry date are not mentioned.  But this does not really happen and so merchants will have to implement measures to secure PAN data. Merchants must equip their data network to deal with PAN securely especially when it is transmitted at the POS.

Moreover, PCI DSS requirement 3.4 states that all merchants must use one of the following techniques to render PAN unreadable. This requirement applies when the PAN Data is stored or when the data is at rest anywhere including portable digital media, backup media, and logs. The techniques of rendering the PAN data unreadable includes

  • Strong cryptography of the PAN
  • PAN truncation (removal of the middle digits),
  • Index tokens and pads
  • Key-management processes

PCI DSS requirement 3.3 specifically requires the PAN data to be masked whenever on display. So, this way, the only digits of the PAN that may be visible are the first six and last four digits. With this only authorized businesses with legitimate commercial needs can see the rest of the information.

 

Final Thought

Despite all the clarity given in terms of the possible threat with storing PAN data nearly 65% of the merchants continue to store unencrypted PAN data on their servers and network. Further, what adds to the problem is that merchants are not able to handle and appropriately secure these stored PAN and cardholder data. Understanding the importance of PAN data and securing them is crucial. This is to prevent incidents of breach and theft. So, the only possible way to prevent this is by implementing measures of defense for handling such sensitive data. Ensuring that the PAN is  protected using one-way hashing or truncation methodologies is one way of assuring the customer’s security of the cardholder data. This way it would also help businesses ensure maintaining PCI DSS Compliance and securing sensitive data.

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